Activity DescriptionFor this assignment you will undertake an analysis based on a self-designed fictitious study that utilizes statistical analyses. You will first develop a fictitious problem to examine. It can be anything. For example, maybe you want to look at whether scores on a standardized college placement test (such as the SAT) are related to the level of income a person makes 10 years after college, or whether those who participate in a Leadership Training program were later rated as better managers compared to those who did not take the training, or whether political affiliation is related to gender. These are just a few examples. Be creative and think about what piques your interest. You might also address a problem that you may want to examine in future research for a thesis or dissertation.You will use Excel to conduct the analysis. Write an analysis report in which you include the following:1. Describe your research study.2. State a hypothesis.3. List and explain the variables you would collect in this study. There must be a minimum of three (3) variables and two (2) must meet the assumptions for a correlational analysis.4. Create a fictitious data set that you will analyze. The data should have a minimum of 30 cases, but not more than 50 cases.5. Conduct a descriptive data analysis that includes the following: a) a measure of central tendency; b) a measure of dispersion and c) at least one graph.6. Briefly interpret the descriptive data analysis.7. Conduct the appropriate statistical test that will answer your hypothesis. It must be a statistical test covered in this course such as regression analysis, single t-test, independent t-test, cross-tabulations, Chi-square, or One-Way ANOVA. Explain your justification for using the test based on the type of data and the level of measurement that the data lends to for the statistical analysis.8. Report and interpret your findings. Use APA style and include a statement about whether you reject or fail to reject the null hypothesis.9. Copy and paste your Excel data output to include it as an appendix to your document submission.10. Remember, the goal of this project is to show what you have learned in the course. Therefore, this project becomes a cumulative learning project where you can demonstrate what you have learned through all the previous assignments, readings and video presentations that you have watched.Support your paper with a minimum of five (5) scholarly resources. In addition to these specified resources, other appropriate scholarly resources, including older articles, may be included.Length: 10-12 pages not including title and reference pages, may include spreadsheetsUSE 2 articles included & 3 additional1Introduction:Signature Assignment: A Statistical StudyThe signature assignment, as the cliché goes, is where “rubber meets the road.” Throughout this course, you were exposed to severalstatistical theories and methods to evaluate hypotheses. It is now time to display your competence of the knowledge you have acquired. Thesignature assignment for this course provides an opportunity for you to apply your skills and creativity to a self­designed fictitious study thatemploys statistical analyses and requires you to use your computational, analytical, and interpretive skills.Review the resources listed in the Books and Resources area below to prepare for this week’s assignments.Books and Resources for this Week:BooksReferenceStatistical reasoning for everyday life.InstructionReview Chaptersas neededDocument/OtherReferenceKahn, J. (2010). Reporting Statistics in APA Style.http://my.ilstu.edu/~jhkahn/apastats.html#1 ArticleReporting Statistics in APA StyleDr. Jeffrey Kahn, Illinois State UniversityThe following examples illustrate how to report statistics in the text of aresearch report. You will note that significance levels in journal articles-especially in tables–are often reported as either “p > .05,” “p < .05," "p < .01," or "p < .001." APA style dictates reporting the exact p value within thetext of a manuscript (unless the p value is less than .001).Please pay attention to issues of italics and spacing. APA style is very preciseabout these. Also, with the exception of some p values, most statistics shouldbe rounded to two decimal places.Mean and Standard Deviation are most clearly presented in parentheses:The sample as a whole was relatively young (M = 19.22, SD = 3.45).The average age of students was 19.22 years (SD = 3.45).Percentages are also most clearly displayed in parentheses with no decimalplaces:InstructionRead Article2Nearly half (49%) of the sample was married.Chi-Square statistics are reported with degrees of freedom and sample sizein parentheses, the Pearson chi-square value (rounded to two decimal places),and the significance level:The percentage of participants that were married did not differ bygender, ?2(1, N = 90) = 0.89, p = .35.T Tests are reported like chi-squares, but only the degrees of freedom are inparentheses. Following that, report the t statistic (rounded to two decimalplaces) and the significance level.There was a significant effect for gender, t(54) = 5.43, p < .001, with menreceiving higher scores than women.ANOVAs (both one-way and two-way) are reported like the t test, but thereare two degrees-of-freedom numbers to report. First report the betweengroups degrees of freedom, then report the within-groups degrees of freedom(separated by a comma). After that report the F statistic (rounded off to twodecimal places) and the significance level.There was a significant main effect for treatment, F(1, 145) = 5.43, p = .02,and a significant interaction, F(2, 145) = 3.24, p = .04.Correlations are reported with the degrees of freedom (which is N-2) inparentheses and the significance level:The two variables were strongly correlated, r(55) = .49, p < .01.Regression results are often best presented in a table. APA doesn't say muchabout how to report regression results in the text, but if you would like toreport the regression in the text of your Results section, you should at leastpresent the unstandardized or standardized slope (beta), whichever is moreinterpretable given the data, along with the t-test and the correspondingsignificance level. (Degrees of freedom for the t-test is N-k-1 where k equalsthe number of predictor variables.) It is also customary to report thepercentage of variance explained along with the corresponding F test.Social support significantly predicted depression scores, ??= -.34, t(225) =6.53, p < .001. Social support also explained a significant proportion ofvariance in depression scores, R2 = .12, F(1, 225) = 42.64, p < .001.3Tables are useful if you find that a paragraph has almost as many numbers aswords. If you do use a table, do not also report the same information in thetext. It's either one or the other.Based on:American Psychological Association. (2010). Publication manual of theAmerican Psychological Association (6th ed.). Washington, DC: Author.Reporting Results of Common Statistical Tests in APA Format. (2010).http://web.psych.washington.edu/writingcenter/writingguides/pdf/stats.pdf# 2 ArticleUniversity of WashingtonPsychology Writing Centerhttps://www.appessaywriters.com/write-my-paper/psych.uw.edu/psych.php#p=339Box 351525psywc@uw.edu(206) 685-8278Copyright 2010, University of Washington stats.pdfRead Article4Reporting Results of Common Statistical Tests in APA FormatThe goal of the results section in an empirical paper is to report theresults of the data analysis used to test ahypothesis. The results section should be in condensed format andlacking interpretation. Avoid discussing whyor how the experiment was performed or alluding to whether yourresults are good or bad, expected orunexpected, interesting or uninteresting. This document isspecifically about how to report statistical results.Refer to our handout “Writing an APA Empirical (lab) Report” fordetails on writing a results section.Every statistical test that you report should relate directly to ahypothesis. Begin the results section by restatingeach hypothesis, then state whether your results supported it, thengive the data and statistics that allowed you todraw this conclusion.If you have multiple numerical results to report, it’s often a good ideato present them in a figure (graph) or atable (see our handout on APA table guidelines).In reporting the results of statistical tests, report the descriptivestatistics, such as means and standard deviations,5as well as the test statistic, degrees of freedom, obtained value of thetest, and the probability of the resultoccurring by chance (p value). Test statistics and p values should berounded to two decimal places. Allstatistical symbols that are not Greek letters should be italicized (M,SD, N, t, p, etc.).When reporting a significant difference between two conditions,indicate the direction of this difference, i.e.which condition was more/less/higher/lower than the othercondition(s). Assume that your audience has aprofessional knowledge of statistics. Don’t explain how or why youused a certain test unless it is unusual.p valuesThere are two ways to report p values. One way is to use the alphalevel (the a priori criterion for theprobablility of falsely rejecting your null hypothesis), which istypically .05 or .01. Example: F(1, 24) = 44.4, p< .01. You may also report the exact p value (the a posterioriprobability that the result that you obtained, or onemore extreme, occurred by chance). Example: t(33) = 2.10, p = .03. Ifyour exact p value is less than .001, it isconventional to state merely p < .001. If you report exact p values,state early in the results section the alphalevel used as a significance criterion for your tests. Example: “Weused an alpha level of .05 for all statistical6tests.”EXAMPLESReporting a significant single sample t-test (µ ? µ0):Students taking statistics courses in psychology at the University ofWashington reported studying more hoursfor tests (M = 121, SD = 14.2) than did UW college students in ingeneral, t(33) = 2.10, p = .034.Reporting a significant t-test for dependent groups (µ1 ? µ2):Results indicate a significant preference for pecan pie (M = 3.45, SD =1.11) over cherry pie (M = 3.00, SD =.80), t(15) = 4.00, p = .001.Reporting a significant t-test for independent groups (µ1 ? µ2):UW students taking statistics courses in Psychology had higher IQscores (M = 121, SD = 14.2) than did thosetaking statistics courses in Statistics (M = 117, SD = 10.3), t(44) = 1.23,p = .09.Over a two-day period, participants drank significantly fewer drinks inthe experimental group (M= 0.667, SD =Copyright 2010, University of Washington stats.pdf1.15) than did those in the wait-list control group (M= 8.00, SD= 2.00), t(4) = -5.51, p=.005.Reporting a significant omnibus F test for a one­way ANOVA:An analysis of variance showed that the effect of noise was significant, F(3,27) = 5.94, p = .007. Post hoc7analyses using the Scheffé post hoc criterion for significance indicated that the average number of errorswassignificantly lower in the white noise condition (M = 12.4, SD = 2.26) than in the other two noiseconditions(traffic and industrial) combined (M = 13.62, SD = 5.56), F(3, 27) = 7.77, p = .042.Reporting tests of a priori hypotheses in a multi­group study:Tests of the four a priori hypotheses were conducted using Bonferroni adjusted alpha levels of .0125 pertest(.05/4). Results indicated that the average number of errors was significantly lower in the silencecondition (M =8.11, SD = 4.32) than were those in both the white noise condition (M = 12.4, SD = 2.26), F(1, 27) = 8.90,p =.011 and in the industrial noise condition (M = 15.28, SD = 3.30), F(1, 27) = 10.22, p = .007. The pairwisecomparison of the traffic noise condition with the silence condition was non-significant. The averagenumber oferrors in all noise conditions combined (M = 15.2, SD = 6.32) was significantly higher than those in thesilencecondition (M = 8.11, SD = 3.30), F(1, 27) = 8.66, p = .009.Reporting results of major tests in factorial ANOVA; non­significant interaction:Attitude change scores were subjected to a two-way analysis of variance having two levels of messagediscrepancy (small, large) and two levels of source expertise (high, low). All effects were statisticallysignificantat the .05 significance level.The main effect of message discrepancy yielded an F ratio of F(1, 24) = 44.4, p < .001, indicating that themeanchange score was significantly greater for large-discrepancy messages (M = 4.78, SD = 1.99) than forsmalldiscrepancymessages (M = 2.17, SD = 1.25). The main effect of source expertise yielded an F ratio of F(1, 24)= 25.4, p < .01, indicating that the mean change score was significantly higher in the high-expertisemessagesource (M = 5.49, SD = 2.25) than in the low-expertise message source (M = 0.88, SD = 1.21). Theinteractioneffect was non-significant, F(1, 24) = 1.22, p > .05.Reporting results of major tests in factorial ANOVA; non­significant interaction:A two-way analysis of variance yielded a main effect for the diner’s gender, F(1, 108) = 3.93, p < .05,such thatthe average tip was significantly higher for men (M = 15.3%, SD = 4.44) than for women (M = 12.6%, SD=6.18). The main effect of touch was non-significant, F(1, 108) = 2.24, p > .05. However, the interactioneffectwas significant, F(1, 108) = 5.55, p < .05, indicating that the gender effect was greater in the touchconditionthan in the non-touch condition.Reporting the results of a chi­square test of independence:A chi-square test of independence was performed to examine the relation between religion and collegeinterest.The relation between these variables was significant, X2 (2, N = 170) = 14.14, p <.01. Catholic teens werelesslikely to show an interest in attending college than were Protestant teens.8Reporting the results of a chi­square test of goodness of fit:A chi-square test of goodness-of-fit was performed to determine whether the three sodas were equallypreferred.Preference for the three sodas was not equally distributed in the population, X2 (2, N = 55) = 4.53, p < .05.Thanks to Laura Little, Ph.D., UW Department of Psychology, for providing the examples reported hereMGT5028-8 > Hypothesis Testing, T-Tests, Cross-Tabulations, and Chi-SquareWeek 8 Assignment: Create and Analyze a Self-Designed Statistical StudyActivity DescriptionFor this assignment you will undertake an analysis based on a self-designed fictitious studythat utilizes statistical analyses. You will first develop a fictitious problem to examine. It can beanything. For example, maybe you want to look at whether scores on a standardized collegeplacement test (such as the SAT) are related to the level of income a person makes 10 years aftercollege, or whether those who participate in a Leadership Training program were later rated asbetter managers compared to those who did not take the training, or whether political affiliationis related to gender. These are just a few examples. Be creative and think about what piques yourinterest. You might also address a problem that you may want to examine in future researchfor a thesis or dissertation.You will use Excel to conduct the analysis. Write an analysis report in which you include thefollowing:1. Describe your research study.2. State a hypothesis.3. List and explain the variables you would collect in this study. There must be aminimum of three (3) variables and two (2) must meet the assumptions for acorrelational analysis.4. Create a fictitious data set that you will analyze. The data should have a minimumof 30 cases, but not more than 50 cases.5. Conduct a descriptive data analysis that includes the following: a) a measure ofcentral tendency; b) a measure of dispersion and c) at least one graph.6. Briefly interpret the descriptive data analysis.7. Conduct the appropriate statistical test that will answer your hypothesis. It must bea statistical test covered in this course such as regression analysis, single t-test,independent t-test, cross-tabulations, Chi-square, or One-Way ANOVA. Explainyour justification for using the test based on the type of data and the level ofmeasurement that the data lends to for the statistical analysis.98. Report and interpret your findings. Use APA style and include a statement aboutwhether you reject or fail to reject the null hypothesis.9. Copy and paste your Excel data output to include it as an appendix to yourdocument submission.10. Remember, the goal of this project is to show what you have learned in the course.Therefore, this project becomes a cumulative learning project where you candemonstrate what you have learned through all the previous assignments, readingsand video presentations that you have watched.Support your paper with a minimum of five (5) scholarly resources. In addition to thesespecified resources, other appropriate scholarly resources, including older articles, may beincluded.Length: 10-12 pages not including title and reference pages, may include spreadsheetsYour response should demonstrate thoughtful consideration of the ideas and concepts that arepresented in the course and provide new thoughts and insights relating directly to this topic. Yourresponse should reflect scholarly writing and current APA standards where appropriate. Be sureto adhere to Northcentral University’s Academic Integrity Policy.Learning Outcomes9.0 Determine alpha (p-values) values and interpret p-levels as related to statistical significance.10.0 Utilize statistical software such as Excel to conduct data analysis.11.0 Analyze the use and applicability of statistics in personal, professional, and academicapplications, and as a tool for research.

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